In the Model creation step, you get sample models: one default regression model, one default decision tree model, and optionally by a benchmark model or models. During modeling, you can add more models and save them. A good practice is to create each type of model and compare their key characteristics.
step, check the following data:
- To verify the predictive performance achieved by the model based on the development set, check the Development set column.
- To verify the predictive performance achieved by the model based on the test set, check the Test set column.
- To verify the predictive performance achieved by the model based on the validation set, check the Validation set column.
- To verify the number of predictors used in the model, check the # Predictors column.
- To verify the list of predictors in the model, check the Predictors column.
- Creating a regression model
Create a model that works well on linear data.
- Creating a decision tree model
Create a model that works well on mid-volume, highly non-linear data.
- Creating a bivariate model
Build a model with two predictors that have univariate performance.
- Creating a genetic algorithm model
Create a genetic algorithm model while you are building predictive models to generate highly predictive, non-linear models. A genetic algorithm solves optimization problems by creating a generation of possible solutions to the problem.
- Computation models
The process of model development allows you to create such default models as regression, decision tree, genetic algorithm, and bivariate.